This will make it easier to test for expected errors in unit tests since
we can compare based on the field values rather than the message (which
might change over time). See https://github.com/openai/codex/pull/8298
for an example.
It also ensures more consistency in the way a `ConstraintError` is
constructed.
https://github.com/openai/codex/pull/8235 introduced `ConfigBuilder` and
this PR updates all call non-test call sites to use it instead of
`Config::load_from_base_config_with_overrides()`.
This is important because `load_from_base_config_with_overrides()` uses
an empty `ConfigRequirements`, which is a reasonable default for testing
so the tests are not influenced by the settings on the host. This method
is now guarded by `#[cfg(test)]` so it cannot be used by business logic.
Because `ConfigBuilder::build()` is `async`, many of the test methods
had to be migrated to be `async`, as well. On the bright side, this made
it possible to eliminate a bunch of `block_on_future()` stuff.
# External (non-OpenAI) Pull Request Requirements
Before opening this Pull Request, please read the dedicated
"Contributing" markdown file or your PR may be closed:
https://github.com/openai/codex/blob/main/docs/contributing.md
If your PR conforms to our contribution guidelines, replace this text
with a detailed and high quality description of your changes.
Include a link to a bug report or enhancement request.
# External (non-OpenAI) Pull Request Requirements
Before opening this Pull Request, please read the dedicated
"Contributing" markdown file or your PR may be closed:
https://github.com/openai/codex/blob/main/docs/contributing.md
If your PR conforms to our contribution guidelines, replace this text
with a detailed and high quality description of your changes.
Include a link to a bug report or enhancement request.
Constrain `approval_policy` through new `admin_policy` config.
This PR will:
1. Add a `admin_policy` section to config, with a single field (for now)
`allowed_approval_policies`. This list constrains the set of
user-settable `approval_policy`s.
2. Introduce a new `Constrained<T>` type, which combines a current value
and a validator function. The validator function ensures disallowed
values are not set.
3. Change the type of `approval_policy` on `Config` and
`SessionConfiguration` from `AskForApproval` to
`Constrained<AskForApproval>`. The validator function is set by the
values passed into `allowed_approval_policies`.
4. `GenericDisplayRow`: add a `disabled_reason: Option<String>`. When
set, it disables selection of the value and indicates as such in the
menu. This also makes it unselectable with arrow keys or numbers. This
is used in the `/approvals` menu.
Follow ups are:
1. Do the same thing to `sandbox_policy`.
2. Propagate the allowed set of values through app-server for the
extension (though already this should prevent app-server from setting
this values, it's just that we want to disable UI elements that are
unsettable).
Happy to split this PR up if you prefer, into the logical numbered areas
above. Especially if there are parts we want to gavel on separately
(e.g. admin_policy).
Disabled full access:
<img width="1680" height="380" alt="image"
src="https://github.com/user-attachments/assets/1fb61c8c-1fcb-4dc4-8355-2293edb52ba0"
/>
Disabled `--yolo` on startup:
<img width="749" height="76" alt="image"
src="https://github.com/user-attachments/assets/0a1211a0-6eb1-40d6-a1d7-439c41e94ddb"
/>
CODEX-4087
refactor the way we load and manage skills:
1. Move skill discovery/caching into SkillsManager and reuse it across
sessions.
2. Add the skills/list API (Op::ListSkills/SkillsListResponse) to fetch
skills for one or more cwds. Also update app-server for VSCE/App;
3. Trigger skills/list during session startup so UIs preload skills and
handle errors immediately.
Changes the `writable_roots` field of the `WorkspaceWrite` variant of
the `SandboxPolicy` enum from `Vec<PathBuf>` to `Vec<AbsolutePathBuf>`.
This is helpful because now callers can be sure the value is an absolute
path rather than a relative one. (Though when using an absolute path in
a Seatbelt config policy, we still have to _canonicalize_ it first.)
Because `writable_roots` can be read from a config file, it is important
that we are able to resolve relative paths properly using the parent
folder of the config file as the base path.
## Problem
The introduction of `notify_sandbox_state_change()` in #7112 caused a
regression where the blocking call in `Session::new()` waits for all MCP
servers to fully initialize before returning. This prevents the TUI
event loop from starting, resulting in `McpStartupUpdateEvent` messages
being emitted but never consumed or displayed. As a result, the app
appears to hang during startup, and users do not see the expected
"Booting MCP server: {name}" status line.
Issue: [#7827](https://github.com/openai/codex/issues/7827)
## Solution
This change moves sandbox state notification into each MCP server's
background initialization task. The notification is sent immediately
after the server transitions to the Ready state. This approach:
- Avoids blocking `Session::new()`, allowing the TUI event loop to start
promptly.
- Ensures each MCP server receives its sandbox state before handling any
tool calls.
- Restores the display of "Booting MCP server" status lines during
startup.
## Key Changes
- Added `ManagedClient::notify_sandbox_state()` method.
- Passed sandbox_state to `McpConnectionManager::initialize()`.
- Sends sandbox state notification in the background task after the
server reaches Ready status.
- Removed blocking notify_sandbox_state_change() methods.
- Added a chatwidget snapshot test for the "Booting MCP server" status
line.
## Regression Details
Regression was bisected to #7112, which introduced the blocking
behavior.
---------
Co-authored-by: Michael Bolin <bolinfest@gmail.com>
Co-authored-by: Michael Bolin <mbolin@openai.com>
1. Skills load once in core at session start; the cached outcome is
reused across core and surfaced to TUI via SessionConfigured.
2. TUI detects explicit skill selections, and core injects the matching
SKILL.md content into the turn when a selected skill is present.
- Make Config.model optional and centralize default-selection logic in
ModelsManager, including a default_model helper (with
codex-auto-balanced when available) so sessions now carry an explicit
chosen model separate from the base config.
- Resolve `model` once in `core` and `tui` from config. Then store the
state of it on other structs.
- Move refreshing models to be before resolving the default model
This is a step towards removing the need to know `model` when
constructing config. We firstly don't need to know `model_info` and just
respect if the user has already set it. Next step, we don't need to know
`model` unless the user explicitly set it in `config.toml`
## Summary
- restore the previous status header when a non-error event arrives
after a stream retry
- add a regression test to ensure the reconnect banner clears once
streaming resumes
## Testing
- cargo fmt -- --config imports_granularity=Item
- cargo clippy --fix --all-features --tests --allow-dirty -p codex-tui
- NO_COLOR=0 cargo test -p codex-tui *(fails: vt100 color assertion
tests expect colored cells but the environment returns Default colors
even with NO_COLOR cleared and TERM/COLORTERM set)*
------
[Codex
Task](https://chatgpt.com/codex/tasks/task_i_69337f8c77508329b3ea85134d4a7ac7)
# External (non-OpenAI) Pull Request Requirements
Before opening this Pull Request, please read the dedicated
"Contributing" markdown file or your PR may be closed:
https://github.com/openai/codex/blob/main/docs/contributing.md
If your PR conforms to our contribution guidelines, replace this text
with a detailed and high quality description of your changes.
Include a link to a bug report or enhancement request.
# External (non-OpenAI) Pull Request Requirements
Before opening this Pull Request, please read the dedicated
"Contributing" markdown file or your PR may be closed:
https://github.com/openai/codex/blob/main/docs/contributing.md
If your PR conforms to our contribution guidelines, replace this text
with a detailed and high quality description of your changes.
Include a link to a bug report or enhancement request.
- This PR wires `with_remote_overrides` and make the
`construct_model_families` an async function
- Moves getting model family a level above to keep the function `sync`
- Updates the tests to local, offline, and `sync` helper for model
families
- Introduce `with_remote_overrides` and update
`refresh_available_models`
- Put `auth_manager` instead of `auth_mode` on `models_manager`
- Remove `ShellType` and `ReasoningLevel` to use already existing
structs
## Updating the `execpolicy` TUI flow
In the TUI, when going through the command approval flow, codex will now
ask the user if they would like to whitelist the FIRST unmatched command
among a chain of commands.
For example, let's say the agent wants to run `apple | pear` with an
empty `execpolicy`
Neither apple nor pear will match to an `execpolicy` rule. Thus, when
prompting the user, codex tui will ask the user if they would like to
whitelist `apple`.
If the agent wants to run `apple | pear` again, they would be prompted
again because pear is still unknown. when prompted, the user will now be
asked if they'd like to whitelist `pear`.
Here's a demo video of this flow:
https://github.com/user-attachments/assets/fd160717-f6cb-46b0-9f4a-f0a974d4e710
This PR also removed the `allow for this session` option from the TUI.
## Refactor of the `execpolicy` crate
To illustrate why we need this refactor, consider an agent attempting to
run `apple | rm -rf ./`. Suppose `apple` is allowed by `execpolicy`.
Before this PR, `execpolicy` would consider `apple` and `pear` and only
render one rule match: `Allow`. We would skip any heuristics checks on
`rm -rf ./` and immediately approve `apple | rm -rf ./` to run.
To fix this, we now thread a `fallback` evaluation function into
`execpolicy` that runs when no `execpolicy` rules match a given command.
In our example, we would run `fallback` on `rm -rf ./` and prevent
`apple | rm -rf ./` from being run without approval.
this PR enables TUI to approve commands and add their prefixes to an
allowlist:
<img width="708" height="605" alt="Screenshot 2025-11-21 at 4 18 07 PM"
src="https://github.com/user-attachments/assets/56a19893-4553-4770-a881-becf79eeda32"
/>
note: we only show the option to whitelist the command when
1) command is not multi-part (e.g `git add -A && git commit -m 'hello
world'`)
2) command is not already matched by an existing rule
- This PR treats the `ModelsManager` like `AuthManager` and propagate it
into the tui, replacing the `builtin_model_presets`
- We are also decreasing the visibility of `builtin_model_presets`
based on https://github.com/openai/codex/pull/7552
- Introduce `openai_models` in `/core`
- Move `PRESETS` under it
- Move `ModelPreset`, `ModelUpgrade`, `ReasoningEffortPreset`,
`ReasoningEffortPreset`, and `ReasoningEffortPreset` to `protocol`
- Introduce `Op::ListModels` and `EventMsg::AvailableModels`
Next steps:
- migrate `app-server` and `tui` to use the introduced Operation
the `/approvals` popup fails to recognize that the CLI is in
WorkspaceWrite mode if that policy has extra bits, like `writable_roots`
etc.
This change matches the policy, ignoring additional config aspects.
Michael BolinandGitHub
xl-openaiandGitHub
jif-oaiandGitHub
Ahmed IbrahimandGitHub
gt-oaiandGitHub
Victor VannaraandGitHub
pakrym-oaiandGitHub
Eric TrautandGitHub
Gav VermaandGitHub
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